Interactive Navigation Beyond Reachability: Benchmark and Agentic Framework

Existing embodied navigation tasks typically assume that traversability, topology, and target observation conditions remain fixed throughout navigation. In real environments, however, an agent can alter the navigation state through physical interaction to affect reachability, target visibility/accessibility, and navigation efficiency. We extend the scope of interactive navigation towards comprehensive state-changing environments, where the agent reasons beyond mere reachability to determine whether interaction is needed, select a task-relevant interaction object, and exploit the resulting change to continue toward the goal. To this end, we introduce MultINav-Bench, a 3K-episode ObjectGoal benchmark that pairs channel interactions (reachability) with container interactions (target visibility/accessibility), includes interaction-necessity annotations, and evaluates both navigation success and interaction-process effectiveness. Beyond the frozen ObjectGoal benchmark, the same interaction-state representation supports our implementation of PointGoal and grounded InstructionGoal tasks, as well as light and full data-collection modes. We further propose MultINav Agent, a three-stage agentic framework that provides semantic interaction guidance through a state-conditioned interaction graph: semantic interactive perception grounds rooms, interaction states, and expected effects; a vision-language model selects the semantically optimal interaction-navigation action; and execution outcomes verify state transitions, update graph relations, and trigger replanning. We evaluate MultINav Agent against non-interactive and naive-interaction baselines on task success, navigation efficiency, and interaction-process effectiveness.